Security and compliance
Operational AI evidence inside your control boundary.
Driftdog is structured around private deployment, redacted metadata, organization-scoped records, explicit service ownership, typed operational states, and audit-friendly incident, guardrail, and drift history.
Private deployment
Installed in your environment for controlled AI evidence.
For healthcare, financial, and enterprise AI systems, the monitoring layer should live where the sensitive workflow already runs. Drift Dog AI is designed for that private deployment pattern.
Runs inside your on-prem, private-cloud, or hybrid environment
No PHI leaves your environment by default
Stores redacted prompt and response metadata unless policy allows more
Designed for API-key boundaries, local-only operation, retention policy, and audit review
Preserves evidence for compliance review, investigation, and model-governance workflows
Compliance-oriented foundations
The current clean core does not claim certifications. It establishes the product architecture needed for controlled access, data residency, retention policy, audit trails, and evidence review as the platform matures.
Executive evaluation
Review Driftdog against your enterprise AI control requirements.
Walk through deployment posture, baseline evaluation logic, audit evidence, drift detection, hallucination-risk controls, and the operating record required for regulated AI systems.